Software for creating efficient high-density tiling peptide chip designs and performing analysis of peptide microarray data for the identification of antibody epitopes

LAZZATI L; CARMONA SJ; AGÜERO F

Bahia Blanca

Congreso; VI Congreso Argentino de Bioinformatica y Biologia Computacional; 2015

Asociación Argentina de Bioinformática y Biología Computacional (A2B2C)

Background. Recently, we used high-density peptide microarrays to identify antibody epitopes associated to Chagas Disease in its chronic phase [1]. First, a selection of Trypanosoma cruzi proteins was made using a previously publishedstrategy [2] and they were classified in five groups. Eight chips with this design were then synthesized andassayed with different pools of antibody samples from Chagas Disease (infected) patients and healthy donors,using a sequential incubation protocol, first with the negative (uninfected) and then with the positive pooledsamples, which produced two fluorescence readouts per chip.Software for analyzing microarray data. Signal processing and normalization were needed to reconstruct the antibody binding profile for each protein. For this purpose, a software package (see Figure 1) has been developed, which consists of four main scripts: written in R (3) and in Perl (1). The software takes as input the readouts from the assay and a user-curated list of antigens and known epitopes. In a first step, data is normalized, so that the signal of both reads is at a common scale. Then, each sequence is mapped to their parental protein and it is checked if it also correspondsto a known antigen. After that, we perform a smoothing procedure on the data to remove outliers. Finally, wecreate graphic plots of the antibody-binding signal profile for each protein that display the negative signal, thecumulative one, and the subtraction of both. For antigens with known epitopes, we also report an AUC and asensitivity-specificity curve.Software for creating efficient chip designs. We also developed a first version of a new pipeline for designing new microarrays (see Figure 2). This software is also written in R and Perl, and its aim is to assign all the peptides in a proteome of interest (we used that of Trypanosoma cruzi) to the minimum number of chip sectors as possible, using hierarchical clustering based on the number of peptides in common between two proteins to group them. The pipeline takes as input the proteins to be included in the arrays and produces one file per sector, containing the list of sequences that should be assigned to them. We reduced the number of sectors needed from 83 to 64.Conclusion. Software for data analysis has been adapted by accepting configuration parameters from a configuration file.References. 1. Carmona SJ, Nielsen M, Schafer-Nielsen C, Mucci J, Altcheh J, Balouz V, Tekiel V, Frasch AC, CampetellaO, Buscaglia CA et al: Towards high-throughput immunomics for infectious diseases: use of next-generation peptide microarrays for rapid discovery and mapping of antigenic determinants. Mol CellProteomics 2015,7:1871-84.2. Carmona SJ, Sartor PA, Leguizamón MS, Campetella OE, Agüero F: Diagnostic peptide discovery:prioritization of pathogen diagnostic markers using multiple features. PLOS One 2012, 12: e50748.